The limits of today’s cellular communication systems are constantly being tested by
the exponential increase in mobile data traffic, a trend which is poised to continue
well into the next decade. Densification of cellular networks, by overlaying smaller
cells, i.e., micro, pico and femtocells, over the traditional macrocell, is seen as an
inevitable step in enabling future networks to support the expected increases in data
rate demand. Next generation networks will most certainly be more heterogeneous
as services will be offered via various types of points of access (PoAs). Indeed, besides
the traditional macro base station, it is expected that users will also be able to
access the network through a wide range of other PoAs: WiFi access points, remote
radio-heads (RRHs), small cell (i.e., micro, pico and femto) base stations or even
other users, when device-to-device (D2D) communications are supported, creating
thus a multi-tiered network architecture. This approach is expected to enhance the
capacity of current cellular networks, while patching up potential coverage gaps.
However, since available radio resources will be fully shared, the inter-cell interference
as well as the interference between the different tiers will pose a significant
challenge. To avoid severe degradation of network performance, properly managing
the interference is essential. In particular, techniques that mitigate interference such
Inter Cell Interference Coordination (ICIC) and enhanced ICIC (eICIC) have been
proposed in the literature to address the issue. In this thesis, we argue that interference
may be also addressed during radio resource scheduling tasks, by enabling
the network to make interference-aware resource allocation decisions.
Carrier aggregation technology, which allows the simultaneous use of several
component carriers, on the other hand, targets the lack of sufficiently large portions
of frequency spectrum; a problem that severely limits the capacity of wireless networks.
The aggregated carriers may, in general, belong to different frequency bands,
and have different bandwidths, thus they also may have very different signal propagation
characteristics. Integration of carrier aggregation in the network introduces
additional tasks and further complicates interference management, but also opens
up a range of possibilities for improving spectrum efficiency in addition to enhancing
capacity, which we aim to exploit. In this thesis, we first look at the resource allocation in problem in dense multitiered
networks with support for advanced features such as carrier aggregation and
device-to-device communications. For two-tiered networks with D2D support, we
propose a centralised, near optimal algorithm, based on dynamic programming principles,
that allows a central scheduler to make interference and traffic-aware scheduling
decisions, while taking into consideration the short-lived nature of D2D links.
As the complexity of the central scheduler increases exponentially with the number
of component carriers, we further propose a distributed heuristic algorithm to tackle
the resource allocation problem in carrier aggregation enabled dense networks. We
show that the solutions we propose perform significantly better than standard solutions
adopted in cellular networks such as eICIC coupled with Proportional Fair
scheduling, in several key metrics such as user throughput, timely delivery of content
and spectrum and energy efficiency, while ensuring fairness for backward compatible
devices.
Next, we investigate the potentiality to enhance network performance by enabling
the different nodes of the network to reduce and dynamically adjust the
transmit power of the different carriers to mitigate interference. Considering that
the different carriers may have different coverage areas, we propose to leverage this
diversity, to obtain high-performing network configurations. Thus, we model the
problem of carrier downlink transmit power setting, as a competitive game between
teams of PoAs, which enables us to derive distributed dynamic power setting algorithms.
Using these algorithms we reach stable configurations in the network,
known as Nash equilibria, which we show perform significantly better than fixed
power strategies coupled with eICIC.

The limits of today’s cellular communication systems are constantly being tested bythe exponent...ial increase in mobile data traffic, a trend which is poised to continuewell into the next decade. Densification of cellular networks, by overlaying smallercells, i.e., micro, pico and femtocells, over the traditional macrocell, is seen as aninevitable step in enabling future networks to support the expected increases in datarate demand. Next generation networks will most certainly be more heterogeneousas services will be offered via various types of points of access (PoAs). Indeed, besidesthe traditional macro base station, it is expected that users will also be able toaccess the network through a wide range of other PoAs: WiFi access points, remoteradio-heads (RRHs), small cell (i.e., micro, pico and femto) base stations or evenother users, when device-to-device (D2D) communications are supported, creatingthus a multi-tiered network architecture. This approach is expected to enhance thecapacity of current cellular networks, while patching up potential coverage gaps.However, since available radio resources will be fully shared, the inter-cell interferenceas well as the interference between the different tiers will pose a significantchallenge. To avoid severe degradation of network performance, properly managingthe interference is essential. In particular, techniques that mitigate interference suchInter Cell Interference Coordination (ICIC) and enhanced ICIC (eICIC) have beenproposed in the literature to address the issue. In this thesis, we argue that interferencemay be also addressed during radio resource scheduling tasks, by enablingthe network to make interference-aware resource allocation decisions.Carrier aggregation technology, which allows the simultaneous use of severalcomponent carriers, on the other hand, targets the lack of sufficiently large portionsof frequency spectrum; a problem that severely limits the capacity of wireless networks.The aggregated carriers may, in general, belong to different frequency bands,and have different bandwidths, thus they also may have very different signal propagationcharacteristics. Integration of carrier aggregation in the network introducesadditional tasks and further complicates interference management, but also opensup a range of possibilities for improving spectrum efficiency in addition to enhancingcapacity, which we aim to exploit. In this thesis, we first look at the resource allocation in problem in dense multitierednetworks with support for advanced features such as carrier aggregation anddevice-to-device communications. For two-tiered networks with D2D support, wepropose a centralised, near optimal algorithm, based on dynamic programming principles,that allows a central scheduler to make interference and traffic-aware schedulingdecisions, while taking into consideration the short-lived nature of D2D links.As the complexity of the central scheduler increases exponentially with the numberof component carriers, we further propose a distributed heuristic algorithm to tacklethe resource allocation problem in carrier aggregation enabled dense networks. Weshow that the solutions we propose perform significantly better than standard solutionsadopted in cellular networks such as eICIC coupled with Proportional Fairscheduling, in several key metrics such as user throughput, timely delivery of contentand spectrum and energy efficiency, while ensuring fairness for backward compatibledevices.Next, we investigate the potentiality to enhance network performance by enablingthe different nodes of the network to reduce and dynamically adjust thetransmit power of the different carriers to mitigate interference. Considering thatthe different carriers may have different coverage areas, we propose to leverage thisdiversity, to obtain high-performing network configurations. Thus, we model theproblem of carrier downlink transmit power setting, as a competitive game betweenteams of PoAs, which enables us to derive distributed dynamic power setting algorithms.Using these algorithms we reach stable configurations in the network,known as Nash equilibria, which we show perform significantly better than fixedpower strategies coupled with eICIC.